Diachronic Embeddings for People in the News

Felix Hennig, Steven Wilson


Abstract
Previous English-language diachronic change models based on word embeddings have typically used single tokens to represent entities, including names of people. This leads to issues with both ambiguity (resulting in one embedding representing several distinct and unrelated people) and unlinked references (leading to several distinct embeddings which represent the same person). In this paper, we show that using named entity recognition and heuristic name linking steps before training a diachronic embedding model leads to more accurate representations of references to people, as compared to the token-only baseline. In large news corpus of articles from The Guardian, we provide examples of several types of analysis that can be performed using these new embeddings. Further, we show that real world events and context changes can be detected using our proposed model.
Anthology ID:
2020.nlpcss-1.19
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
173–183
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpcss-1.19
DOI:
10.18653/v1/2020.nlpcss-1.19
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpcss-1.19.pdf